1. Field of Art
The invention relates generally to computing a person's cognitive function and, more specifically, to unobtrusive assessment of cognitive function from mobile device usage.
2. Background Art
An aging society and increasing lifespan has resulted in an increased prevalence of cognitive decline in society from mild cognitive impairment to Alzheimer's disease. Today, one in eight older Americans has Alzheimer's disease and Alzheimer's is the sixth leading cause of death in the United States [1]. By 2025, the number of Americans age 65 and older with Alzheimer's disease is estimated to increase 30% and by 2050 that number is expected to triple, barring any breakthroughs to prevent, slow or arrest the disease [1]. Prior to developing Alzheimer's disease, patients go through a six-year prodromal phase of cognitive decline. The societal burden of mental disease in the elderly is staggering and poised to worsen.
Repeat studies have demonstrated that a healthy lifestyle of moderate physical activity, good diet, and social interaction can preserve cognitive function and reverse cognitive decline [2,3,4]. Several of these studies use group comparisons between intervention and control groups and rely on tests of cognitive function pre-intervention and post-intervention to assess the intervention effects on cognitive function [5,6]. But for most individuals, knowing that healthy habits preserve cognitive function is not sufficiently motivating until it is too late and the damage is well underway. By the time a family member or physician first discovers signs of cognitive impairment the subject has suffered substantial atrophy and loss of key structural brain regions necessary for memory, learning, executive function, and attention. Neuropsychological tests administered by a neurologist show high specificity for cognitive impairment but low sensitivity.
The emergence of online tests available through many application vendors such as BrainBaseline [7] may help detect signs of impairment earlier but only if the subject is highly motivated and persistent in testing themselves repeatedly and regularly for years. Highly motivated individuals are more likely to have healthy habits including physical activity, diet, and social interaction and least likely to benefit from close surveillance of online behavioral tests, further limiting the value of those tests to the broader segment of society that would benefit most. In addition, test practice effects are well documented [8,9] whereby the subject develops test taking skills that increase their scores but do not transfer well to real world activities and further undermine the test's sensitivity and specificity to cognitive decline.
The introduction of mobile devices and their broad adoption has revolutionized how society interacts both with each other and with their surroundings. A smartphone today enables a user to make calls, send and receive emails and text messages, find their location on a map or retrieve directions to a destination point, browse the internet, download and play game applications, and a host of other activities. In addition, these smartphones are equipped with accelerometers and gyroscopes that sense the device's acceleration and orientation in 3-dimensions. Processing of the acceleration and orientation signals reveals the user's activity such as whether the person is walking or jogging.
One company that has leveraged the close interaction of an individual with their mobile device to make behavioral assessments is Ginger.io [10,11]. Ginger.io provides a smartphone application that tracks the number and frequency of calls, text messages, and emails sent, and uses the device's global positioning system (GPS) and accelerometer to infer activity level. The target population for Ginger.io's application is patients with chronic diseases such as diabetes, mental disease, and Chron's disease. When a patient deviates from their routine calling and texting patterns, Ginger.io alerts the individual's caregiver to intervene and assess the situation for noncompliance with medications, inappropriate titration of medications, and other factors that may precipitate a flare-up of the patient's disease.
In Ginger.io's application, changes in routine calling, texting, or location are interpreted as symptoms of disease flare-up requiring investigation and notably have high false positives rates. Cognitive decline due to aging is an insidious process over several years where subtle declines in physical activity, diet changes, and social engagement are causal, not symptomatic. Diagnosing cognitive decline requires frequent assessment of the higher order cognitive processes of executive function, working memory, episodic memory, and attention.
What is needed is a method and system to assess cognitive function that is highly sensitive, specific, and unobtrusive to an individual.                1. Alzheimer's Association, 2012 Alzheimer's Disease Facts and Figures. www.alz.org/downloads/facts_figures_2012.pdf        2. Christopher Hertzog, et al., Enrichment Effects on Adult Cognitive Development: Can the Functional Capacity of Older Adults Be Preserved and Enhanced? Association for Psychological Science, 2009, 9(1):1-65        3. Interview with Nicholas Spitzer, Crosswords don't make you clever. The Economist, August, 2013, http://www.economist.com/blogs/prospero/2013/08/quick-study-neuroscience        4. Gretchen Reynolds, How exercise can help us learn. New York Times, August 2013, http://well.blogs.nytimes.com/2013/08/07/how-exercise-can-help-us-learn/?_r=0        5. Interview with J. Carson Smith, Exercise may be the best medicine for Alzheimer's disease. Science Daily, July 2013, http://www.sciencedaily.com/releases/2013/07/130730123249.htm        6. J. Carson Smith, et al., Interactive effects of physical activity and APOE-e4 on BOLD semantic memory activation in healthy elders. Neuroimage, January 2011; 54(1):635-644        7. www.brainbaseline.com        8. Ackerman P L, Individual differences in skill learning: An integration of psychometric and information processing perspectives. Psychol Bull, 1987, 102:3-27        9. Healy A F, Wohldmann E L, Sutton E M, Bourne L E, Jr, Specificity effects in training and transfer of speeded responses. J Exp Psychol Learn Mem Cognit, 2006, 32:534-546        10. www.ginger.io        11. Owen Covington, ‘Virtual nurse’ helps Forsyth Medical Center track diabetics. The Business Journal, May 2013, http://www.bizjournals.com/triad/news/2013/05/20/forsyth-medical-center-using-virtual.html        